Robust Optimal Sliding-Mode Tracking Control for a Class of Uncertain Nonlinear MIMO Systems

This paper addresses the problem of tracking a reference trajectory asymptotically given by a linear time-varying exosystem for a class of uncertain nonlinear MIMO systems based on the robust optimal sliding-mode control. The nonlinear MIMO system is transformed into a linear one by the input-output linearization technique, and at the same time the input-output decoupling is realized. Thus, the tracking error equation is established in a linear form, and the original nonlinear tracking problem is transformed into an optimal linear quadratic regulator (LQR) tracking problem. A LQR tracking controller (LQRTC) is designed for the corresponding nominal system, and the integral sliding-mode strategy is used to robustify the LQRTC. As a result, the original system exhibits global robustness to the uncertainties, and the tracking dynamics is the same as that of LQRTC for the nominal system. So a robust optimal sliding-mode tracking controller (ROSMTC) is realized. The proposed controller is applied to a two-link robot system, and simulation results show its effectiveness and superiority

Plasma reforming of naphthalene as a tar model compound of biomass gasification

The contamination of producer gas with tars from biomass gasification remains a significant challenge in the bioenergy industry and a critical barrier, limiting the commercial applications of biomass gasification. Non-thermal and non-equilibrium plasma offers an unconventional and emerging technology for the effective reduction of problematic tars from gasification. In this study, we investigated plasma reforming of naphthalene as a two-ring tar model compound using a gliding arc discharge (GAD) reactor with/without steam. The influence on the plasma conversion of naphthalene based on the inlet naphthalene concentration, discharge power and steam-to-carbon ratio was examined to understand the effects of these operating parameters on the destruction of tar, gas selectivity/yield and energy efficiency. Adding H2O in the plasma process generates oxidative OH radicals, creating additional reaction routes for the step-wised oxidation of naphthalene and its fragments towards the CO, CO2 and water. The optimum ratio (2.0) of steam-to-carbon was identified to achieve the highest naphthalene conversion (84.8%), C2H2 yield (33.0%), total gas yield (72.2%) and energy efficiency (5.7 g/kWh). The effect of the amount of steam on the plasma reduction of tars was dependent on the balance between two opposite effects due to the presence of steam: positive effect of OH radicals and the negative effect of electron attachment on water molecules. Introducing an appropriate amount of steam to the plasma reduction of naphthalene also substantially minimized the formation of by-products and enhanced the carbon balance. Plausible reaction mechanisms for the plasma decomposition of naphthalene were proposed through a comprehensive analysis of gaseous and condensable products combined with plasma spectroscopic diagnostics

Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory

This paper attempts to mine the hidden individual behavior pattern from the raw users’ trajectory data. Based on DBSCAN, a novel spatio-temporal data clustering algorithm named Speed-based Clustering Algorithm was put forward to find slow-speed subtrajectories (i.e., stops) of the single trajectory that the user stopped for a longer time. The algorithm used maximal speed and minimal stopping time to compute the stops and introduced the quantile function to estimate the value of the parameter, which showed more effectively and accurately than DBSCAN and certain improved DBSCAN algorithms in the experimental results. In addition, after the stops are connected with POIs that have the characteristic of an information presentation, the paper designed a POI-Behavior Mapping Table to analyze the user’s activities according to the stopping time and visiting frequency, on the basis of which the user’s daily regular behavior pattern can be mined from the history trajectories. In the end, LBS operators are able to provide intelligent and personalized services so as to achieve precise marketing in terms of the characteristics of the individual behavior.</p

An availability model based on a three-stage failure process under age based replacement

This paper proposes a joint optimal policy of inspection and age based replacement based on a three-stage failure process to jointly optimize the inspection and replacement intervals. The three-stage failure process divides the failure process of system into three stages: namely normal, minor defective and severe defective. When the minor defective stage is identified, the subsequent inspection interval is halved. Once identifying the severe defective stage, the maintenance action is carried out immediately. The system is replaced once it reaches the certain age. Finally, a numerical example is presented to demonstrate the efficiency of the proposed model

An adaptive neuro fuzzy hybrid control strategy for a semiactive suspension with magneto rheological damper

The main function of a vehicle suspension system is to isolate the vehicle body from external excitation in order to improve passenger comfort and road holding and to stabilise its movement. This paper considers the implementation of an adaptive neuro fuzzy inference system (ANFIS) with a fuzzy hybrid control technique to control a quarter vehicle suspension system with a semiactive magneto rheological (MR) damper. A quarter car suspension model is set up with an MR damper and a semiactive controller consisting of a fuzzy hybrid skyhook-groundhook controller and an ANFIS model is also designed. The fuzzy hybrid controller is used to generate the desired control force, and the ANFIS is designed to model the inverse dynamics of MR damper in order to obtain a desired current. Finally, numerical simulations of the semiactive suspensions with the ANFIS- hybrid controller, the traditional hybrid controller, and passive suspension are compared. The results of simulations show that the proposed ANFIS-hybrid controller provides better isolation performance than the other controllers

Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students' proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multi-objective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models' variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models.Comment: 15 pages, 12 figures, 5 table